Support vector regression applied to the determination of
the developmental age of a <i>Drosophila</i> embryo from its
segmentation gene expression patterns

Myasnikova, Ekaterina; Samsonova, A. A.; Samsonova, Maria; Reinitz, John

doi:10.1093/bioinformatics/18.suppl_1.s87

Cited by 24 publications

(15 citation statements)

References 18 publications

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“…It includes image segmentation, background removal, determination of developmental age of an embryo, image registration and data averaging. [4][5][6][7][8][9] These methods can be applied both sequentially and independently and joined in different combinations. The main advantages of the pipeline are in a wide range of processing methods, automatic performance, flexibility in use and ease of adaptation to other problems.…”

Section: Discussionmentioning

confidence: 99%

“…We have developed a method for staging of an embryo on the basis of its gene expression pattern by applying SV regression. 7 The method is fast and automatic, shows good prediction accuracy and thus constitute a great advance in staging of fixed Drosophila embryos.…”

Section: Discussionmentioning

confidence: 99%

“…To automate the process of embryo staging we have developed a method to predict the embryo age from its gene expression pattern. 7 The method is based on the analysis of the highly dynamic expression pattern of the eve gene, which is visualized in each embryo, and standardization of these expression patterns against a small training set of embryos with a known developmental age. As a prediction method we use the support vector regression (SVR) method.…”

Section: Determination Of Developmental Agementioning

confidence: 99%

“…Results presented in Figure 7 show that the predicted ages correlate well with the temporal classification of embryos. 7 …”

Section: Construction Of the Regression Functionmentioning

confidence: 99%

“…We present an overview of each method of the pipeline in order to provide a reader with general ideas how to extract quantitative data from confocal images, while the detailed information about the processing algorithms may be found in our publications. [4][5][6][7][8][9] We also discuss how each of the methods designed can be adapted to deal with images of other gene expression patterns. …”

mentioning

confidence: 99%

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Pipeline for acquisition of quantitative data on segmentation gene expression from confocal images

Surkova

Myasnikova²,

Janssens³

et al. 2008

Fly

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We describe a data pipeline developed to extract the quantitative data on segmentation gene expression from confocal images of gene expression patterns in Drosophila. The pipeline consists of five steps: image segmentation, background removal, temporal characterization of an embryo, data registration and data averaging. This pipeline was successfully applied to obtain quantitative gene expression data at cellular resolution in space and at the 6.5-minute resolution in time, as well as to construct a spatiotemporal atlas of segmentation gene expression. Each data pipeline step can be easily adapted to process a wide range of images of gene expression patterns. IntroductionBiology is increasingly asking quantitative questions. Quantification is essential to understand the principles of organism functioning. Modern physics and engineering provide tools and strategies for accurate measurements and the acquisition of comprehensive and consistent data sets. For example, microarrays are widely used to quantify the levels of gene expression, 1 and fluorescence restoration after photobleaching (FRAP) is applied to measure diffusion rate or molecular transport. 2 In the last decade developmental biology has achieved tremendous progress. Due to the success of genetics and functional genomics a large number of genes controlling development has been cloned and sequenced, the products of these genes have been identified, and the function of many of these molecules has been revealed. However, despite of these spectacular achievements the integrative picture of how an organism controls the phenotype of tissues and organs is still absent.The morphogenetic field is a basic unit of ontogeny. 3 This physically detached area is formed by complex coordinated interactions of transcripts and proteins that make up the field. Understanding the developmental processes within the morphogenetic field requires a quantitative characterization of the dynamics of each of the field components.Currently available methods for quantification of gene expression like microarrays, quantitative PCR and CAT assays have some limitations for investigations of developmental processes. All of these methods are based on the preparation of homogenates of cells and are unable to capture spatial information about gene expression.The study of development in morphogenetic fields requires methods for the acquisition of gene expression data that will allow monitoring the expression time course of all of the genes simultaneously at the resolution of a single cell. Confocal scanning microscopy of fluorescently tagged molecules constitute the most common data acquisition strategy, which preserves spatial information about the distribution of gene products. This method involves the recognition of protein or RNA in situ by target-specific primary antibodies or a labeled antisense RNA probe. The detection method is usually based on the use of secondary antibodies conjugated with a fluorophore. Confocal microscopy provides high quality digital images ready for computer pr...

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Determination Of Developmental Agementioning

confidence: 99%

“…Results presented in Figure 7 show that the predicted ages correlate well with the temporal classification of embryos. 7 …”

Section: Construction Of the Regression Functionmentioning

confidence: 99%

mentioning

confidence: 99%

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Pipeline for acquisition of quantitative data on segmentation gene expression from confocal images

Surkova

Myasnikova²,

Janssens³

et al. 2008

Fly

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Prediction of protein B‐factor profiles

2005

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The polypeptide backbones and side chains of proteins are constantly moving due to thermal motion and the kinetic energy of the atoms. The B-factors of protein crystal structures reflect the fluctuation of atoms about their average positions and provide important information about protein dynamics. Computational approaches to predict thermal motion are useful for analyzing the dynamic properties of proteins with unknown structures. In this article, we utilize a novel support vector regression (SVR) approach to predict the B-factor distribution (B-factor profile) of a protein from its sequence. We explore schemes for encoding sequences and various settings for the parameters used in SVR. Based on a large dataset of high-resolution proteins, our method predicts the B-factor distribution with a Pearson correlation coefficient (CC) of 0.53. In addition, our method predicts the B-factor profile with a CC of at least 0.56 for more than half of the proteins. Our method also performs well for classifying residues (rigid vs. flexible). For almost all predicted B-factor thresholds, prediction accuracies (percent of correctly predicted residues) are greater than 70%. These results exceed the best results of other sequence-based prediction methods.

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Removal of background signal from in situ data on the expression of segmentation genes in Drosophila

et al. 2005

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Here we present a method for the removal of nonspecific background signal from fluorescently localized expression patterns of Drosophila segmentation genes. Our algorithm for removal of background signal brings the data to a common standard form with zero background and removes systematic error in gene expression levels caused by the presence of background. The method is based on the discovery, reported here, that background is well fit by a very broad two-dimensional paraboloid. The paraboloid is determined from the area of the embryo in which a given gene is not expressed and the whole pattern is then normalized by this paraboloid to remove background from the entire embryo. The software implementing this algorithm is available from the authors.

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Support vector regression applied to the determination of the developmental age of a Drosophila embryo from its segmentation gene expression patterns

Cited by 24 publications

References 18 publications

Pipeline for acquisition of quantitative data on segmentation gene expression from confocal images

Pipeline for acquisition of quantitative data on segmentation gene expression from confocal images

Prediction of protein B‐factor profiles

Removal of background signal from in situ data on the expression of segmentation genes in Drosophila

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